professor izhak rubin electrical engineering department ucla august 2005 [email protected]

Professor Izhak Rubin

Professor Izhak Rubin Electrical Engineering Department

UCLAAugust 2005

[email protected]

Unmanned-Vehicle Aided Multi-Tier

Autonomous Intelligent Wireless Networks:

Mobile Backbone Networks

mailto:[email protected]

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Professor Izhak RubinFORCEnet Architecture using AINS TechnologiesDevelopment of AINS system architecture for realizing FORCEnet using intelligent autonomous collaborating agents embedded in entities that perform communications networking, sensing, maneuvering and striking functions.

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Professor Izhak RubinAINS Innovative Networking Technologies enable a Network-Centric C4ISR Operation

Development of survivable and autonomously adaptable mobile communications network systems that support high quality transport of critical messaging flows and real-time streams in an adverse environment to enable network centric combat operations and warfare.

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Our Approach Breakthrough methods to guide intelligent

platforms to rapidly mitigate network system gaps, substantially re-constitute degraded configurations and enhance performance, at the right place at the right time.

Such methods include the autonomous layout and control of unmanned networked platform formations and UAV swarms in a multi-tier hierarchical mobile backbone networked infrastructure, and the formation of internets-in-the-sky.

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Our Innovative Networking Technologies: I

UV aided Mobile Backbone Networks (MBNs): Multi-tier adaptive autonomous networking

Robust survivable QoS Routing for mobile ad hoc wireless networks employing multi tier UV swarms

Architecture, infrastructure and approaches for the configuration of UAV platforms and swarms to jointly best support

Communications networking Sensing tasks Area search and surveillance

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Our Innovative Networking Technologies: II Power-control spatial-reuse Medium

Access Control (MAC) protocols and algorithms Integrated MAC scheduling, power control

and routing leading to significant enhancements in the throughput efficiency of shared radio channels

Integrated System Management (ISM) New paradigm in the design of system

management architecture that combines monitoring, control and resource allocations for C4ISR systems

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Professor Izhak RubinRobust Wireless Networking – Architecture and topology Synthesis Synthesis of a multi-tier (land, air

and sea based) mobile backbone network (MBN) New distributed algorithms to configure

the multi tier backbone network Dynamical adaptivity to failures,

application mixes and capacity requirements

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ANet 3

Hierarchical Configuration of UV-aided Mobile

Backbone Network (UV-MBN)

Backbone NodeGateway

ANet 1

ANet 2

ASPN 1ASPN 2


AINS based UV-aided Dynamically Reconfigurable

Network

UV aided Mobile Backbone Network Protocol (MBNP)

Quality of Service (QoS) UV-aided operation MBN based On Demand Routing with Flow Control (MBNR-FC) Swarm Networking

Fig. 4. Sample of Flow Blocking rates for flows of different classes using the IRI QoS based admission control mechanism

Mbns.exe

mbns.exe mbns.exe

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Illustration of our heterogeneous Mobile Backbone Network (MBN)

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Professor Izhak RubinUV aided Autonomous Mobile Backbone Network

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Backbone Construction

(a) (b)

(c) (d)

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The MBN Topology Synthesis Algorithm (TSA)

Neighbor Discovery Every node exchange “Hello

Message” periodically. – Short timer

Every node updates its neighbor list periodically. – Long timer

Each node learns its 1-hop neighbor information and 2-hop BN neighbor information.

Association Algorithm Every node that is in a BCN state

or RN state attempts to associate with a BN with highest Weight.

The Weight of a node can be based on its ID, degree, congestion level, and a nodal/link stability measure.

If no acceptable neighboring BN is detected, try BCNs; If no BCN either, try RNs

HelloHello

HelloHello

HelloHello

HelloHelloHelloHello

HelloHelloHello

HelloHelloHello

HelloHello(BCN: 2,3)

Hello Message: ID, Weight, BN Neighbor List

(BCN: 1,3)

(BCN: 1,2,5,7)

(BCN: 3,6)

(BCN: 4,5,7)

(BCN: 6,7)

(BCN: 3,4,6)

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The MBN Topology Synthesis Algorithm (TSA)

BCN to BN Conversion Algorithm(1) Client coverage:

a BCN that receives an association request from a BCN or RN, converts itself to a BN.

(2) Connectivity of the BNet: A BCN node finds that by converting itself to a BN it will upgrade the Bnet connectivity.

BN to BCN Conversion Algorithm (1) All of its BN neighbors have at

least one common BN neighbor whose weight is higher than the weight of the underlying BN that is considering to convert.

(2) Each of its BCN clients have at least one other BN neighbor.

BN BN

BN

(BCN: 2,3)

(BCN: 1,3)

(BCN: 1,2,5,7)

(BCN: 3,6)

(BCN: 4,5,7)

(BCN: 6,7)

(BCN: 3,4,6)

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MBN Topology Synthesis Algorithm Convergence Time

The MBN topology synthesis algorithm convergence in constant time, of the order of O(1).

Convergence Time

0

5

10

15

20

25

100 200 300 400 500Number of Nodes

Num

ber o

f Cyc

les

TSA

w/ Rule 1 & 2 bound

Dai & Wu

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Total number of backbone nodes (BNs) in the network

The backbone network (Bnet) size is independent of the number of nodes in the network or the nodal density.

The backbone network (Bnet) size is only proportional to the area size.

Backbone Network Size

0

10

20

30

40

50

60

70

80

100 200 300 400 500Number of Nodes

Num

ber o

f BN

s

TSA

Minimum

Dai & Wu

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Control Message Overhead of TSA

The control message overhead of TSA is independent of the number of nodes in the network or the nodal density.

Hello Message Rate (per node)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

100 200 300 400 500Number of Nodes

Rat

e (K

bps)

TSA

Dai & Wu

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Data Delivery Radio of 25 UDP flows

Data Delivery Ratio

0

20

40

60

80

100

100 200 300 400 500Number of Nodes

Del

ieve

ry R

atio

(%)

AODV

w/ Rule 1 & 2

Dai & Wu

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Average End-to-end Delay Performance

Average End-to-End Delay

0.0

0.5

1.0

1.5

2.0

2.5

3.0

100 200 300 400 500Number of Nodes

Del

ay (s

)AODV

w/ Rule 1 & 2

Dai & Wu

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Average Data Path LengthAverage Path Length

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

100 200 300 400 500Number of Nodes

Num

ber o

f Hop

sAODV

w/ Rule 1 & 2

Dai & Wu

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Average Path Length We expect the employment of the MBNR scheme to yield a longer average path

length value than that obtained under AODV (since routes are now established only across the backbone network). Interestingly, our simulation results indicate that the MBNR protocol does not always produce longer path lengths.

RREQ packets are transmitted as broadcast packets, when such a packet experiences collision, no MAC layer retransmission takes place. Consequently, if the network is already overwhelmed by RREQ storm, it is likely that a route will not be discovered in time or that a “non-shortest path route” will be selected

Average Path Length

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 3 6 9 12 15 18 21

BN Neighbor Limit

Path

Len

gth

(hop

s)

100 nodes200 nodes300 nodes

Average Path Length

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 3 6 9 12 15 18 21

BN Neighbor Limit

Path

Len

gth

(hop

s)

100 nodes200 nodes300 nodes

(a) Stationary network (b) Mobile network

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Professor Izhak RubinQoS based Robust Scalable Routing (MBNR)

MBN based Robust Routing protocols (MBNR) On-demand routing mechanism that uses selective control packet

forwarding (across the MBN) to discover routes Proactive routing for route establishment in smaller subnets and certain Access Nets

Unique MBN based Flow and Congestion control mechanism (MBNR-FC protocol) to preserve the quality of service (QoS) of established flows and to ensure that, under overloading conditions, only high priority flows are supported at desired QoS

Unique cross physical, MAC and network layer algorithms and protocols to ensure that the realistic nature of the wireless radio environment is dynamically incorporated into communications resource allocations and routing operations.

Effective use of UGV and UAV swarms to establish backbone routes and to distribute control packets

Hybrid backbone and non-backbone routing and flow/congestion control to efficiently utilize resources in areas that are not covered or are away from the mobile backbone and its UGV and UAV agents

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MBN Routing with Flow Control (MBNR-FC):Delay Jitter Performance Comparison among Different Protocols

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Network Performance: packet delay and delay jitter

Delay jitter vs. Traffic loading The delay jitter is reduced as traffic loading rate is increased (when

the network is not saturated). Explanation: route discovery produces a larger delay which is different from the delay experienced when the route is available.

When the network is congested, more route discovery attempts take place.

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Hybrid Routing Strategy Capacity utilization of pure MBNR-FC

When the number of BCNs is not able to form a backbone to cover the whole network area, backbone-only paths will limit the overall throughput capacity of the network.

Allowing both backbone routing and non-backbone routing could fully utilize the network capacity.

Long-distance traffic vs. Short-distance traffic Short-distance traffic obtains shorter path lengths by

routing through all type of nodes, while long-distance traffic does not.

Long-distance traffic obtains routing overhead reduction by routing through backbone network, while short-distance traffic does not.

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Delay-throughput performance of MBNR-FC/DA under 2-hop

Anets The delay-throughput performance with distance thresholds equal to 7 hops and

9-hops demonstrate a significant throughput capacity gain compared to that with distance threshold equal to 0-hops (which is obtained by pure MBNR-FC).

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Under Development: Adaptive Scheme for Distance threshold Selection

Adaptive scheme for distance threshold selection Execute in a distributed manner. Adjust the distance threshold according to the current traffic distribution.

Procedures: Each BN collects the congestion information of its own Anet: the number

of clients that are not eligible for participating in the route discovery process (i.e.; if they or their neighbors are congested.)

BNs that are within 2 hops from each other exchange their Anet congestion indices.

The obtained congestion information is used by each BN to compute a distance threshold dth which it broadcasts to its Anet clients

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Dr. Izhak Rubin

High Capacity QoS MAC Power-control spatial-reuse Medium

Access Control (MAC) protocols and algorithms Integrated MAC scheduling, power

control and routing leading to significant enhancements in the throughput efficiency of shared radio channels

Provision of quality of service (QoS) by prioritized scheduling and cross layer MAC/Networking operations

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Dr. Izhak Rubin

MAC Mechanisms Power control spatial reuse (PCSR)

Medium Access Control (MAC) layer operations Scheduling based QoS based MAC

mechanisms (such as: PCSR demand assigned TDMA / FDMA / CDMA)

Random access based PCSR techniques providing enhanced performance

Directional and omnidirectional operations PHY-MIMO driven power control MAC

operations Autonomous power control MAC

operations using UAV swarms

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8

9

1BN

2

64

3

7

5

Power: 1mWPower: 10mWPower: 50mWPower: 100mW

7 550mW

2 150mW

4 550mW

9 850mW

6 950mW

BN 710mW

BN 310mW

Slot 9Slot 8

2 61mW

9 110mW

BN 310mW

1 450mW

4 210mW

BN 310mW

1 910mW

6 410mW

BN 710mW

8 950mW

8 7100mW

9 250mW

6 150mW

5 350mW

3 550mW

Slot 6Slot 5Slot 3

9 110mW

Slot 1

2 410mW

BN 710mW

Slot 10Slot 7Slot 4Slot 2

Power ControlSpatial-ReuseMAC DA/TDMA

large increase

in spatial reuse factor

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Throughput Analysis of our Power Control Scheduling Algorithm (PCSA) and

alternative scheme (TPA) (for an illustrative network

with 10 active nodes)

0

0.5

1

1.5

2

2.5

3

3.5

0 0.02 0.04 0.06 0.08 0.11 0.15 0.19 0.23 0.27

Packet Generation Rate (packets/slot)

Thro

ughp

ut (p

acke

ts/s

lot)

PCSATPA,D=100mTPA,D=250mTPA,D=600mTPA,D=1000m

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Uniform Traffic1000*1000m area, 100 nodes, 30 flows,Fixed Routing In this experiment, we fix

the routing in advance so we can focus on understanding purely the characteristics of the 802.11MAC.

DPC offers a significantly betterThroughput-delay characteristicscompared to low power transmissions (blue) and regular 802.11 with no power control (green).

0

0.5

11.5

2

2.5

33.5

4

4.5

0 200 400 600 800 1000 1200 1400

Throughput (Kbps)

dela

y (s

ec)

Regular DPC LOW

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Localized TrafficBenefits of our distributed power control algorithm are especially apparent when trafficpatterns are localized.

0

0.5

1

1.5

2

2.5

0 500 1000 1500 2000 2500 3000

Throughput (Kbps)

Del

ay (s

ec)

Regular DPC

400*400m area, 100 nodes, 15 flows,Fixed Routing

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Cross Layer Power Control based Topology Synthesis What is the optimal number of APs needed

for best network performance (in terms of throughput, delay, delay-jitter, packet loss ratio)? APs should not only be deployed to provide

coverage but also to accommodate different capacity needs of nodes

What is the optimal power to operate at? When is it useful to employ “Cell Splitting” and

get new APs or “Soft APs” (a laptop configured to work as an AP) into the network?

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Adaptation of AP / BN selection to the traffic profile

Throughput vs. Number of AP

0

5

10

15

20

25

0.5 0.7 0.9 1.1 1.3 1.5Throughput (Mbps)

AP

Short Range Long Range

When using power controlthe number of APs deployedShould depend on theTraffic characteristics in theNetwork.

When the traffic is mostlyLong distance, it’s better toEmploy a fewer number ofAPs, and vice versa.

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On going developments: Simulation Results for Hybrid TDMA/CSMA

Experiment with three APs, 9 flows, 3 of which are inter-AP flows. Case 1: Hybrid schemeCase 2: Regular 802.11We can clearly see that the hybrid scheme delivers significant throughput and delay benefits over the regular, non power controlled IEEE802.11Note: inter-AP flows can traverse paths that are as long as 3 hops

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Professor Izhak RubinIntegrated System Management (ISM)

New paradigm in the design of system management architecture that combines monitoring, control and resource allocations for C4ISR systems

Hierarchical Integrated System Management and control architecture using nodal, subnetwork and system wide monitors and control elements

Monitoring attributes and Management Information Bases (MIBs) for communications, sensing, UV, maneuverable and strike segments

ISM algorithms for joint resource, performance, failure and topology management of MBN based C4ISR systems using UAV swarms

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Integrated System Manager

Integrated NetworkManager - MBN

Integrated NetworkManager - Sensor

Integrated NetworkManager - UAV

BNs RNs GCSNodesUGVs

MIB

MIB MIB

MIBMIB MIB

MIB

MIB MIB

Sensor Proxy UAV Proxy

Cloudcap

ITM1

ITM2

UAV

Integrated System Management: system configuration

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Professor Izhak RubinIntegrated System ManagementIllustration of ISM display of status of communications, sensing and UAV networked systems

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ISM: Topology Display

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ISM: Traffic Graph Display

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Professor Izhak RubinOn-Going & Planned Research Works Power control spatial reuse MACs

Hybrid MAC for meshed architectures Topology Synthesis of the Backbone

Networks Characterization and tuning of the algorithms;

performance features and comparisons; stability and efficiency adaptations

MBN based QoS Routing Development and analysis of the hybrid

MBNR-FC/DA scheme

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Professor Izhak Rubinoutstanding research works UAV and UGV aided networking UAV swarms Cross Layer networking

Distributed cross-layer PCSR MACs Integrated power control MACs and MBN based QoS

routing Phy / MAC / Link / Network and topology synthesis cross

layer protocols and algorithms Performance analyses and simulations under a

multitude of multimedia applications and C4ISR scenarios

Incorporation of QoS oriented network management schemes

Energy aware MBN based networking

professor izhak rubin electrical engineering department ucla august 2005 [email protected]

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